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Experience curves for wind power

Author

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  • Lena Neij
  • Per Dannemand Andersen
  • Michael Durstewitz

Abstract

The objective of this study was to improve the understanding of the use of different experience curves in energy analysis. The study was based on the application of experience curves to wind power systems, but the approach is generic and can be used in other energy technologies. In the study, experience curves for wind power were developed, and it has been demonstrated that different types of experience curves can be developed for one and the same technology. Depending on the system boundaries used, the market perspective used, the time frame, the manufacturers included and the size of the turbines included, the progress ratios of the curves ranged from 83% to 117%, indicating a cost reduction of 0–17% per doubling of produced or installed capacity. We discuss the interpretation of these curves and the most appropriate experience curve for use in energy analysis.

Suggested Citation

  • Lena Neij & Per Dannemand Andersen & Michael Durstewitz, 2004. "Experience curves for wind power," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 15-32.
    • Handle: RePEc:ids:ijetpo:v:2:y:2004:i:1/2:p:15-32
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    Citations

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    Cited by:

    1. Esmaelian, Majid & Tavana, Madjid & Di Caprio, Debora & Ansari, Reza, 2017. "A multiple correspondence analysis model for evaluating technology foresight methods," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 188-205.
    2. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    3. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    4. Söderholm, Patrik & Pettersson, Maria, 2011. "Offshore wind power policy and planning in Sweden," Energy Policy, Elsevier, vol. 39(2), pages 518-525, February.
    5. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    6. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    7. Grafström, Jonas & Lindman, Åsa, 2017. "Invention, innovation and diffusion in the European wind power sector," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 179-191.
    8. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    9. Mauleón, Ignacio & Hamoudi, Hamid, 2017. "Photovoltaic and wind cost decrease estimation: Implications for investment analysis," Energy, Elsevier, vol. 137(C), pages 1054-1065.
    10. Li, Sheng & Zhang, Xiaosong & Gao, Lin & Jin, Hongguang, 2012. "Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: Methodology and case studies," Applied Energy, Elsevier, vol. 93(C), pages 348-356.
    11. Strupeit, Lars & Neij, Lena, 2017. "Cost dynamics in the deployment of photovoltaics: Insights from the German market for building-sited systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 948-960.
    12. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    13. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.

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